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Found 28 papers, 8 papers with code
Self-Adjusting Evolutionary Algorithms Are Slow on Multimodal Landscapes
The one-fifth rule and its generalizations are a classical parameter control mechanism in discrete domains.
Faster Optimization Through Genetic Drift
For the classical benchmark OneMax, the cGA has to two different modes of operation: a conservative one with small step sizes $\Theta(1/(\sqrt{n}\log n))$, which is slow but prevents genetic drift, and an aggressive one with large step sizes $\Theta(1/\log n)$, in which genetic drift leads to wrong decisions, but those are corrected efficiently.
How Population Diversity Influences the Efficiency of Crossover
Our theoretical understanding of crossover is limited by our ability to analyze how population diversity evolves.
Plus Strategies are Exponentially Slower for Planted Optima of Random Height
We compare the $(1,\lambda)$-EA and the $(1 + \lambda)$-EA on the recently introduced benchmark DisOM, which is the OneMax function with randomly planted local optima.
A Tight $O(4^k/p_c)$ Runtime Bound for a ($μ$+1) GA on Jump$_k$ for Realistic Crossover Probabilities
This yields an improved and tight time bound of $O(\mu n \log(k) + 4^k/p_c)$ for a range of~$k$ under the mild assumptions $p_c = O(1/k)$ and $\mu \in \Omega(kn)$.
Hardest Monotone Functions for Evolutionary Algorithms
Recently, Kaufmann, Larcher, Lengler and Zou conjectured that for the self-adjusting $(1,\lambda)$-EA, Adversarial Dynamic BinVal (ADBV) is the hardest dynamic monotone function to optimize.
Analysing Equilibrium States for Population Diversity
We give an exact formula for the drift of population diversity and show that it is driven towards an equilibrium state.
Comma Selection Outperforms Plus Selection on OneMax with Randomly Planted Optima
For certain parameters, the $(1,\lambda)$ EA finds the target in $\Theta(n \ln n)$ evaluations, with high probability (w. h. p.
Tight Runtime Bounds for Static Unary Unbiased Evolutionary Algorithms on Linear Functions
In this paper we investigate how this result generalizes if standard bit mutation is replaced by an arbitrary unbiased mutation operator.
OneMax is not the Easiest Function for Fitness Improvements
In this paper we disprove this conjecture and show that OneMax is not the easiest fitness landscape with respect to finding improving steps.
Population Diversity Leads to Short Running Times of Lexicase Selection
In this paper we investigate why the running time of lexicase parent selection is empirically much lower than its worst-case bound of O(N*C).
Self-adjusting Population Sizes for the $(1, λ)$-EA on Monotone Functions
Recently, Hevia Fajardo and Sudholt have shown that this setup with $c=1$ is efficient on \onemax for $s<1$, but inefficient if $s \ge 18$.
Two-Dimensional Drift Analysis: Optimizing Two Functions Simultaneously Can Be Hard
In this paper we show how to use drift analysis in the case of two random variables $X_1, X_2$, when the drift is approximatively given by $A\cdot (X_1, X_2)^T$ for a matrix $A$.
Runtime analysis of the (mu+1)-EA on the Dynamic BinVal function
We study evolutionary algorithms in a dynamic setting, where for each generation a different fitness function is chosen, and selection is performed with respect to the current fitness function.
Large Population Sizes and Crossover Help in Dynamic Environments
In this paper, we study the effect of larger population sizes for Dynamic BinVal, the extremal form of dynamic linear functions.
Exponential Slowdown for Larger Populations: The $(μ+1)$-EA on Monotone Functions
In particular, it was known that the $(1+1)$-EA and the $(1+\lambda)$-EA can optimize every monotone function in pseudolinear time if the mutation rate is $c/n$ for some $c<1$, but they need exponential time for some monotone functions for $c>2. 2$.
Self-Adjusting Mutation Rates with Provably Optimal Success Rules
The one-fifth success rule is one of the best-known and most widely accepted techniques to control the parameters of evolutionary algorithms.
The linear hidden subset problem for the (1+1) EA with scheduled and adaptive mutation rates
For the first setup, we give a schedule that achieves a runtime of $(1\pm o(1))\beta n \ln n$, where $\beta \approx 3. 552$, which is an asymptotic improvement over the runtime of the static setup.
When Does Hillclimbing Fail on Monotone Functions: An entropy compression argument
Hillclimbing is an essential part of any optimization algorithm.
Bounding Bloat in Genetic Programming
While many optimization problems work with a fixed number of decision variables and thus a fixed-length representation of possible solutions, genetic programming (GP) works on variable-length representations.
Destructiveness of Lexicographic Parsimony Pressure and Alleviation by a Concatenation Crossover in Genetic Programming
We show that the Concatenation Crossover GP can efficiently optimize these test functions, while local search cannot be efficient for all three variants independent of employing bloat control.
A General Dichotomy of Evolutionary Algorithms on Monotone Functions
We study the same question for a large variety of algorithms, particularly for $(1+\lambda)$-EA, $(\mu+1)$-EA, $(\mu+1)$-GA, their fast counterparts like fast $(1+1)$-EA, and for $(1+(\lambda,\lambda))$-GA. We find that all considered mutation-based algorithms show a similar dichotomy for HotTopic functions, or even for all monotone functions.
Sorting by Swaps with Noisy Comparisons
We study sorting of permutations by random swaps if each comparison gives the wrong result with some fixed probability $p<1/2$.
Drift Analysis
Drift analysis is one of the major tools for analysing evolutionary algorithms and nature-inspired search heuristics.
Drift Analysis and Evolutionary Algorithms Revisited
One of the easiest randomized greedy optimization algorithms is the following evolutionary algorithm which aims at maximizing a boolean function $f:\{0, 1\}^n \to {\mathbb R}$.
The (1+1) Elitist Black-Box Complexity of LeadingOnes
We regard the permutation- and bit-invariant version of \textsc{LeadingOnes} and prove that its (1+1) elitist black-box complexity is $\Omega(n^2)$, a bound that is matched by (1+1)-type evolutionary algorithms.
Introducing Elitist Black-Box Models: When Does Elitist Selection Weaken the Performance of Evolutionary Algorithms?
Black-box complexity theory provides lower bounds for the runtime of black-box optimizers like evolutionary algorithms and serves as an inspiration for the design of new genetic algorithms.
OneMax in Black-Box Models with Several Restrictions
Black-box complexity studies lower bounds for the efficiency of general-purpose black-box optimization algorithms such as evolutionary algorithms and other search heuristics.
